Network Intrusion Detection Model Based on Data-Driven Algorithm: A Reivew

Abstract

Network intrusion detection is a key technology for maintaining network security. In the digital era, network attacks are increasingly complex and diverse, and traditional rule-based detection methods are difficult to cope with. Data-driven algorithms demonstrate powerful detection capabilities by analyzing historical data and automatically learning potential patterns. This paper provides a comprehensive overview of the research on network intrusion detection models based on data-driven algorithms. First, it introduces the background and importance of network intrusion detection and explains its key role in guaranteeing network data integrity and service availability. Then, the application of data-driven algorithms in network intrusion detection is discussed in detail, and the principles, advantages and limitations of various algorithms are analyzed from the two dimensions of supervised learning and unsupervised learning. Then, the model performance is evaluated from multiple dimensions such as detection accuracy, precision, recall, F1 score and AUC value, and the performance of different algorithms is compared. The contribution of this paper is to systematically sort out the research lineage in this field, summarize the existing results and shortcomings, and provide a clear starting point for the subsequent research, which helps to improve the intelligence level of network intrusion detection system and enhance the network security protection capability.